Camera traps are an effective tool for monitoring insect–plant interactions

Abstract Insect and pollinator populations are vitally important to the health of ecosystems, food production, and economic stability, but are declining worldwide. New, cheap, and simple monitoring methods are necessary to inform management actions and should be available to researchers around the world. Here, we evaluate the efficacy of a commercially available, close‐focus automated camera trap to monitor insect–plant interactions and insect behavior. We compared two video settings—scheduled and motion‐activated—to a traditional human observation method. Our results show that camera traps with scheduled video settings detected more insects overall than humans, but relative performance varied by insect order. Scheduled cameras significantly outperformed motion‐activated cameras, detecting more insects of all orders and size classes. We conclude that scheduled camera traps are an effective and relatively inexpensive tool for monitoring interactions between plants and insects of all size classes, and their ease of accessibility and set‐up allows for the potential of widespread use. The digital format of video also offers the benefits of recording, sharing, and verifying observations.

prone to misidentification, a lack of verifiable proof in the form of pictures or video, and difficulty identifying insects to a relevant taxonomic level (Kremen et al., 2011;Roy et al., 2016).
Automated camera systems have been found to be a powerful yet underutilized tool for gathering large amounts of high-quality insect data (Gilpin et al., 2017;Lortie et al., 2012;Pegoraro et al., 2020;Steen, 2017). Camera traps have been used to address numerous research questions including pollinator diversity and behavior (e.g., Howard et al., 2021;Manetas & Petropoulou, 2000) and insect predation (Grieshop et al., 2012). However, many of these camera systems use continuous video recording, which produces massive quantities of data, and require adaptations to achieve the desired recording settings or to extend battery life (e.g., Droissart et al., 2021;Lortie et al., 2012;Micheneau et al., 2008;Pegoraro et al., 2020;Steen, 2017). While these more sophisticated, custom systems have been shown to be effective (e.g., Droissart et al., 2021), an evaluation of a more user-friendly, accessible, "out-of-the-box" solution, such as game cameras, would benefit the less technologically savvy practitioner (Droissart et al., 2021;Steen, 2017).
Here, we evaluated the use of high-definition, commercially available game cameras with close-focus functionality to monitor insectplant interactions and behaviors. We then compared the results to traditional human observations. Based on previous studies, we predicted cameras would capture more insect detections than humans in general (Lortie et al., 2012;Pegoraro et al., 2020). However, we further compared two recording settings available on most models of game cameras: scheduled and motion-activated. We predicted the number of detections by cameras would vary based on insect body size. For example, motion-activated cameras would capture more large insects because they would trigger the motion sensor, whereas the scheduled cameras make no use of the motion sensor feature. In addition, we evaluated insect detections by the two camera settings and human observations based on insect behaviors. We did not limit documented behaviors only to pollination behaviors in order to maximize the utility of the technology to answer a variety of research questions such as monitoring of insect diversity and/or interspecific interactions (Morse, 1986;Reed, 1995;Robertson & Maguire, 2005). We predicted that behaviors that result in more time at the flower (e.g., flower probing) would be detected similarly between both cameras and humans, but that fast-moving behaviors (e.g., flying) would be more likely to be detected by cameras. Our goal is to present a proof-of-concept for high-precision insect-plant monitoring that uses relatively inexpensive tools, requires minimal training to carry out, and can be used to address a wide variety of insect-plant research questions.

| Insect monitoring
We conducted a series of 16 paired human and camera trap observation trials to compare game cameras (hereafter, "camera traps") to human observers for documenting insect-plant interactions. Trials took place at five study sites in Champaign County and DuPage County, Illinois, U.S.A., between 1 July and 21 August 2020. Each site was a suburban or exurban residential property containing numerous planted native and ornamental species.
For each trial, we deployed two cameras (Bushnell NatureView HD model 119740, Bushnell Outdoor Products, Overland Park, Kansas), each equipped with a close-range 460 mm lens. Cameras with similar specifications are available from alternate manufacturers (e.g., Reconyx Professional Series cameras with custom focal distance, Reconyx, Holmen, Wisconsin). One camera was set to record one 60-s-long video every 5 min (hereafter, "scheduled" camera) and the other was set to record one 60-s-long video when triggered by motion (hereafter, "motion-activated" camera) (See Supporting Information S2 for video examples). Cameras were mounted on separate tripods and aimed at a focal flower or small cluster of flowers of the same species at a distance of 46 cm (Figure 1). We used the handheld Live View accessory provided with the camera to ensure proper focus and framing of the focal flower part. After activating the pair of cameras, an observer sat 1-2 m from focal flower and observed insect interactions using binoculars, in order to facilitate more accurate insect identification from a distance. Although the majority of pollinator observations are conducted with the naked eye, we found binoculars to be a helpful tool when focusing on one or a few focal flowers in order to compare it to the performance of camera traps. The observer monitored the focal flower for 3 h after activating the cameras and recorded the identity, quantity, and behavior of all insects interacting with the flower. All analyses were conducted with data collected during a 3-h period in which observers and cameras simultaneously monitored the focal points of the plant.
Insect monitoring was conducted using 13 plant species, covering a range of flower morphologies, and included native and nonnative species (Table 1). Flower species diversity was favored over replication in the experimental design in order to display game cameras' performance when compared to humans in a variety application cases. Observations were conducted during daylight hours between 08:57 and 18:14. Trials were not conducted in inclement weather with precipitation or during storms.

| Video annotation
After the observation trials, camera trap videos were reviewed and annotated using the image analysis software Timelapse 2 (Greenberg, 2021). For each video, a trained observer recorded the identity, quantity, and behavior of all insects present. In most cases, the observer who conducted the plant observations also annotated the videos for that trial. Insects were identified taxonomically order level Diptera, Formicidae, Coleoptera, Lepidoptera, Hemiptera, Hymenoptera excluding the family Formicidae, or unknown. Formicidae were considered separately from the rest of Hymenoptera due to its morphological and behavioral differences.
Hymenoptera was further organized into three size categories (small, medium, large) according to body length. The small size category represents insects approximately less than 10 mm in length (e.g., Chrysis spp., Agapostemon spp.), medium size insects are approximately 10-15 mm in length (e.g., Vespula spp., Anthidium spp.) and the large insects are approximately greater than 15 mm in length (e.g., Bombus spp., Xylocopa spp.). Because of variation in life history stages, we categorized Lepidoptera as either adult or larva rather than by size class. Insect behavior was classified into six behaviors: flying, hovering, landing, walking, probing, and moving between flowers. Insect interactions with leaves, stems, and other parts of the focal plant were ignored, as were interactions with flowers that were out of focus or in the background. Insects that were unidentifiable to taxonomic order were included in overall insect counts but excluded from order-specific analyses. F I G U R E 1 Image (a) shows a schematic of insect observation trials. Two camera traps with different recording settings (one scheduled, one motion-activated) were placed side-by-side on separate tripods, 46 cm from the focal flower. A human observer viewed insect-plant interactions occurring on the focal flower using binoculars. Images (b-d) display the camera trap setup with a variety of lighting conditions flower types. Each trial lasted 3 h and took place in Champaign County and DuPage County, Illinois, USA We constructed our model set using the "lme4" package (Bates et al., 2015). We checked for overdispersion using the function by Bolker et al. (2021) and for zero-inflation using the "performance"

TA B L E 1 Number of insect-plant interactions detected on each species of plant monitored during 16 3-h observation trials in Champaign
package (Lüdecke et al., 2021). Overdispersion was detected in many of our count data models (variance-to-mean ratios >2), thus we used a negative binomial distribution (White & Bennetts, 1996) for all models to maintain consistency and comparability among the model set. In cases where an individual model was not overdispersed, use of either a negative binomial distribution or Poisson distribution did not quantitatively and qualitatively affect model results (see Supporting Information S1 for all model summaries and diagnostic tests). We report odds ratios to quantify the effectiveness of each camera type relative to the traditional human observer method. To present odds ratios in a forest plot, we used the packages "ggplot2" (Wickham, 2016) and "ggforestplot" (Scheinin et al., 2021).
We compared the diversity of insects (number of taxonomic orders) detected by the three observation methods using a linear mixed model via the "lme4" package (Bates et al., 2015), again with trial as a random effect. We examined residual plots to confirm that assumptions of linearity and homoskedasticity were not violated. Next, we evaluated the influence of camera type (scheduled versus motion-activated) on the body size of insects detected by creating generalized linear mixed models using a Poisson distribution. We compared detections of small-, medium-, and large-bodied Hymenoptera, as well as larvae and adult Lepidoptera, between the two camera types. Size classes were not documented by human observers during trials, so we could not evaluate body sizes detected by humans in comparison to cameras. Last, we tested for differences in the frequencies that behaviors were observed by scheduled cameras, motion-activated cameras, and human observers using chisquare tests.  (Figure 2). Motion-activated cameras were 0.27 (95% CI = 0.12-0.63) times as likely as human observers to detect Diptera (Figure 2). There were no significant differences between either camera type and humans at detecting Coleoptera or Lepidoptera ( Figure 2).

| DISCUSS ION
Our results demonstrate that commercially available game camera traps are an effective alternative to human observations for documenting insect-plant interactions and insect behaviors. In particular, cameras set to automatically capture video on a set schedule provided significantly higher numbers of detections of Formicidae, Hemiptera, and Hymenoptera than humans, and detected more insects of all sizes than motion-activated cameras.
Our results suggest that even commercially available, unmodified cameras can collect valuable data for insect conservation and management goals.
We predicted that motion-activated cameras would capture more large insects because they would be more likely to activate the motion-sensor feature; however, scheduled cameras still outperformed motion-activated cameras, capturing more insects of all size or age classes of Hymenoptera and Lepidoptera. A marked difference between the scheduled and motion-activated cameras is the motion-activated cameras' 200 ms response time to motion triggers, which may have been too slow for the rapid movement of some flying insects and began to record when the insect was no longer in frame. This may explain in part the disparity between detection rates of motion-activated and scheduled cameras. Due to worldwide concern over pollinator population collapse, the three-fold increase in Hymenoptera detection by scheduled cameras compared to humans may be of interest to researchers who want to survey bee/ wasp presence, richness, and inter-and intra-species interactions.
It should be noted, however, that identification to genus or species for many Hymenoptera is most likely not possible with game camera images ( Figure 4).
Our results suggest that detection rates by the different camera settings and compared to human observations varied by insect behavior. In particular, human observers were more likely to detect insects that were exhibiting flower probing or landing on flowers whereas cameras, particularly those with motion-activated settings, were more likely to detect insects engaged in flying or hovering behaviors. This makes intuitive sense as behaviors that result in more time at the flower (e.g., probing/landing) are more likely to be detected by the human eye whereas high motion behaviors (e.g., flying) may be more likely to be detected by motion-activated cameras. This result would suggest that researchers should consider the type of data of interest when considering the use of cameras for monitoring plant-insect interactions.
Previous studies on insect behavior have used a variety of camera systems with adaptations or alterations that allow them to function similarly to camera traps (Droissart et al., 2021;Lortie et al., 2012;Micheneau et al., 2008;Pegoraro et al., 2020;Steen, 2017

ACK N OWLED G M ENTS
The authors thank David Edlund, Alondra Estrada, Sean Konrath, Laura Whipple, and Angie Wolske for help with data collection. Funding was provided by the Department of Defense, National Defense Center for Energy and Environment, and we are grateful to Jennifer Rawlings and Adrian Salinas for managing the program and supporting our work.

CO N FLI C T O F I NTE R E S T
All authors declare that they have no conflicts of interest.